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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Singh, Vaishali Aruna, M. Somanna, M. N. Varun Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The objective of this research is to provide a cutting-edge security system intended to protect personal information of users in a cloud setting. Controlling access techniques, encoding, and decode procedures are all included in this complex safety framework. A novel key generation procedure that makes use of the cryptography of elliptic curves is presented in order to strengthen security precautions and guarantee the production of very secure keys. Furthermore, a novel Identity-based Elliptic Curve Access Control (Id-EAC) method is put forward for restricting user use of a variety of data kinds. To further improve user privacy in the cloud the surroundings, a novel binary-value-based a two-phase encryption as well as decryption technique is provided. Within the aforementioned safety A system, a Compact Virtual Signatures Mechanism built on a modulo value is presented to guarantee data consistency. Users are assured of high standards of data protection, ease of use, and integrity by the security framework. Research findings highlight the edge of the solutions recommended in the aforementioned framework, demonstrating improved reliability and safety over current approaches. © 2023 IEEE.

关键词： access control Cloud storage data integrity decryption digital signature encryption lightweight digital signature algorithm and performance analysis security architecture

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Addax Optimization Algorithm with Convolutional Neural Network for Rheumatoid Arthritis classification 1

Addax Optimization Algorithm with Convolutional Neural Netwo...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Buvaneswari, Pr. Bhasgi, Shivani S Alabdeli, Haider Soundarya, M. Saravanan, T. Vit Bhopal University School of Electrical and Electronics Engineering Sehore India Maisammaguda School of Computer Science and Engineering Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

The Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, therefore early diagnosis treatment is important. The RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects, it becomes decreases physical function causing articular damage and fatigue. This paper proposed Addax Optimization Algorithm (AOA) with Convolutional Neural Network (CNN) for classification, which is efficiently achieve better accuracy. Initially data obtained from the RA dataset and pre-processing involved the min-max normalization is uniformly the feature vector. The combination of AOA is designed to optimize hyper parameters efficiently which is significantly reduce the high dimensionality and better accuracy in classifying of RA. The classification utilized CNN technique capturing complex patterns in image data effective for analysis the RA image and adaptive approach helps to find the best solution by using optimization approach. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall and f 1-score metrics on RA dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Federated Learning Paradigms in Network Security for Distributed Systems 3

Federated Learning Paradigms in Network Security for Distrib...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Ojha, Anatha Charan Kumar Yadav, Dhananjay Ashwini, B. Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In this work, we present a novel approach for enhancing network security within distributed systems through Federated Learning (FL). The proposed method integrates three fundamental algorithms: Federated Averaging (FedAvg), Secure Aggregation, and Adaptive Learning Rate. FedAvg allows collaborative training of a global model while preserving data privacy on decentralized devices. Secure Aggregation employs cryptographic techniques to maintain confidentiality during model updates' aggregation. Adaptive Learning Rate dynamically adjusts the learning rate, enhancing model optimization efficiency. The proposed work illustrates these algorithms through detailed equations and flowcharts. The proposed approach combines these techniques to achieve secure and collaborative model training across distributed devices, thereby enhancing network security. © 2023 IEEE.

关键词： Network security

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Healthcare Data Analysis using Competitive Ensemble Deep Learning Model

Healthcare Data Analysis using Competitive Ensemble Deep Lea...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Gunavathy, K. Pandeya, Megha Sekar, R. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

By utilising novel techniques to boost classification performance, ML is utilised to obtain healthcare knowledge. These classifiers have the potential to boost model decision and hence boost patient care. Recent research has shown that, compared to mixed model classification, the accuracy of single-model classification is worse. This study delves into the value of DL methods for both organised and unorganised healthcare databases. The exponential growth of healthcare data has made it challenging to mine for meaningful trends. Clinical data volumes are manageable for DL, and the results are improved. To enhance classification efficiency for structured data, a fresh competitive ensemble deep learning model has been presented. When it comes to Twitter sentiment analysis, however, the suggested work emphasises a competitive DL model that can handle unstructured data. The report also covers the planned Capsule Network for text data along with the Gravitational Search Algorithm (GSA). In order to assess the efficacy of the proposed model, it is compared to a more conventional model. The algorithm improved upon previous methods of analysing user sentiment on Twitter. The proposed model outperformed industry standards in the vast majority of tests. Furthermore, for multi-label classification problems, the outcomes are superior in terms of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Advancing Early Detection of Cervical Cancer Through Multi-Model Intelligent Classifiers and Computational Technique 3

Advancing Early Detection of Cervical Cancer Through Multi-M...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhuri, Sumantra Suneetha, K. Acharjya, Kalyan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

One of the most common illnesses in women and one with a high fatality rate is cervical cancer. Early detection using screening techniques helps to increase the lifespan of women and lower mortality rates. According to the World Health Organization (WHO), cervical cancer is now the fourth most common malignancy to affect women. According to surveys, low- and middle-income nations account for 90% of all cancer-related deaths since there is a dearth of knowledge about the disease and its preventative treatments there. When it comes to preserving the health of women, cervical cancer research is one of the most crucial fields of study. Early detection of cervical cancer is crucial because it can save the lives of many women whose lives are at risk. Foretelling cervical cancer growth prior to administering a screening test is the goal of the proposed study, which makes use of data mining and machine learning algorithms. Feature selection methods were used to isolate the characteristics useful for diagnosing cervical cancer. When applied to the dataset, an enhanced SVM model's accuracy was found to be 94.16 percent. This highlights the model's usefulness in screening for a potentially deadly disease like cervical cancer. This type of prediction algorithm for cervical cancer is gaining popularity as a result of its high accuracy. This ensures the model's high level of predictive performance and hints that it could revolutionise the way the needy are assisted if used in real time. © 2023 IEEE.

关键词： Diseases

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Word Embeddings and Document Topics are Combined With Deep Learning in a System for Classifying Videos

Word Embeddings and Document Topics are Combined With Deep L...

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2023 IEEE Global Conference on Information Technologies and communications, GCITC 2023

作者： Nahak, Fakira Mohan Mandalapu, V. L. Manasa Kaur, Ravneet Sarishma Malathy, V. Arri, Harwant Singh Manipal University School of Mass Communication Rajasthan Jaipur India School of Engineering and Technology Department of Ece India Uttaranchal University Law College Dehradun Uttarkhand Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarkhand Dehradun248002 India Sr University School of Engineering Department of Ece Telangana Warangal India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350308167

By empowering continuous discussions among an extraordinary number of clients, interchanges made conceivable by contemporary Web-based open doors have modified the manner in which people trade data today. However, the risks associated with personal assaults that force many individuals to quit a conversation in which they were taking part can occasionally undermine the benefits afforded by such potent tools of communication. Such an issue is associated with supposedly poisonous comments, which incorporate offensive attacks, individual assaults, and all the more extensively, a forceful style of support by many individuals in a discussion that makes a few members leave it. This exploration gives a methodology that might play out a multi-class multi-name order of a discussion inside a range of six classes of poisonousness by using the Apache Hadoop huge information stage and different word embeddings. Scientists test this technique by ordering a dataset of Wikipedia talk page remarks because of a Kaggle task. © 2023 IEEE.

关键词： data deep learning MlLib videos Web-based

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LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of communications and Information Networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

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The Confluence of Edge Computing and Artificial Neural Networks for Real-time Data Processing in IoT Devices

The Confluence of Edge Computing and Artificial Neural Netwo...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Jayabalan, Bhuvana Chaudhary, Chetan Bhagwat, Renuka Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350315127

In this study, we present a novel approach for efficient real-time data processing in IoT devices at the edge by dynamically adapting artificial neural networks (ANNs) based on available computational resources and input data complexity. We address the challenges of resource constraints and latency reduction, crucial for enabling seamless and responsive IoT applications. Our proposed method comprises three main algorithms: Dynamic Neural Network Adaptation (DNNA), Edge Inference Scheduler (EIS), and Latency-aware Activation Thresholding (LAT). Algorithm 1, DNNA, is responsible for dynamically adjusting the complexity of the deployed ANN to match the available resources and data complexity. It optimizes the trade-off between computational efficiency and model accuracy, making the best use of limited edge resources. Algorithm 2, EIS, optimizes the allocation of computational resources while minimizing latency during task scheduling on edge devices. It ensures that tasks are assigned to devices that can meet latency requirements, improving overall system responsiveness. Algorithm 3, LAT, dynamically adjusts activation thresholds within ANNs based on predicted latency, allowing for a fine balance between accuracy and processing speed. This adaptation ensures that the ANN performs optimally under varying conditions. Our experimental results demonstrate the superiority of the proposed method over traditional approaches in terms of latency, inference accuracy, energy efficiency, scalability, privacy, security, resource utilization, and overall efficiency. The proposed method not only reduces latency but also maintains high accuracy levels, making it a valuable solution for real-time data processing in IoT devices at the edge. © 2023 IEEE.

关键词： Neural networks

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An Improved Method for Text Classification Using Contrastive Learning

An Improved Method for Text Classification Using Contrastive...

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Chen, Maojian Luo, Xiong Peng, Qiaojuan Shen, Hailun Huang, Ziyang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Shunde Innovation School University of Science and Technology Beijing Foshan528399 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Ouyeel Co. Ltd. Shanghai201999 China

ISBN: (纸本)9789819961863

Quality objection texts provided by customers are crucial for steel e-commerce platforms. How to classify these texts and quickly provide corresponding solutions is very important. This article presents an approach based on deep learning for classifying steel quality objection text. With the help of deep learning, the proposed model can automatically extract semantic features from unstructured text without manual intervention. Different from traditional methods, to further improve the model’s performance, this article employs contrastive learning to learn more robust representations. The evaluation of this approach on a large dataset of steel quality objection text demonstrates that it outperforms traditional methods and achieves excellent results. The proposed approach enables the identification of quality issues quickly, which is of utmost importance for the steel industrial practical implications. By reducing the time and resources required to identify quality objections, the proposed approach can help to improve the efficiency of the steel industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Large dataset

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts 3

Evaluative Analysis of Secure approaches in Simulated Real-W...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kannagi, A. Chaudhary, Pavan Muthupandi, G. Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape. © 2023 IEEE.

关键词： Cybersecurity

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